Voraussetzungen

Summary

During this course, students engage in the entire process of solving a real-world data science project: from collecting and processing actual data, to applying a suitable and appropriate analytic method to the problem. Both the problem statements for the project assignments and the datasets orginate from the life-science domain, similar to those that students might typically encounter within industry or academic research.

Topics in machine learning, pattern recognition, and statistical modeling. While the mathematical methods and theoretical aspects will be discussed, focus is on algorithmic and practical issues.

Application of theoretical knowledge acquired during the course to a real project involving actual data in a realistic setting.

Practical tools to implement the an actual project.

Big Data Aspects

Big Data requires the storage, organization, and processing of data at a scale and efficiency that go well beyond the capabilities of conventional information technologies. In this course, we will study the state of the art in big data management: we will learn about algorithms, techniques and tools needed to support big data processing.
The projects we will be dealing with will require large data analysis and how it can be implemented on Big Data platforms. There will be programming assignments that will provide students with hands-on experience on building data-intensive applications using existing Big Data platforms.